Title: Predicting Housing Prices Using Linear Regression and Gradient DescentDeadline: $11$th February $2024$Objective:To develop a machine learning model using linear regression that predicts housing prices based on a setof features. You will implement the gradient descent algorithm to optimize the model's parameters.Assignment Details:Part $1$: Theoretical UnderstandingExplain the concept of linear regression in the context of supervised learning.Describe the Loss function in linear regression and its importance.Derive the mathematical formulation for gradient descent in the context of linear regression.Discuss the impact of the learning rate on the convergence of the gradient descent algorithm.Part $2$: Data PreparationDownload the housing dataset from the UCI Machine Learning Repository or any other reputable source.The dataset should include various features like the size of the house, the number of bedrooms, thenumber of floors, age of the house, etc., along with the price.Perform exploratory data analysis to understand the distribution and relationship between features andthe target variable $($price$).$Preprocess the data by handling missing values, encoding categorical variables, and normalizing thefeatures.Part $3$: Model Implementation